Quality-of-Service Routing in Ad-Hoc Networks Using OLSR
Ying Ge, Thomas Kunz, Louise Lamont | |
CRC Carleton University CRC | |
http://kunz-pc.sce.carleton.ca/ | |
tkunz@sce.carleton.ca |
Introduce QoS routing into OLSR | |
Develop three heuristics to enhance OLSR | |
Evaluate heuristics | |
Prove the optimality of two of the heuristics in static network case |
QoS Route Information | ||
Using traditional best-effort Ad-Hoc routing algorithms | ||
Piggybacking QoS information in control messages | ||
Useful for call admission control | ||
QoS Routing Computation | ||
Ticket-based probing | ||
CEDAR | ||
Hierarchically structured multi-clustered organizations |
Selects MPR to cover 2-hop neighbors | |
Exchanges neighbor/MPR information in Hello message | |
Generates and relays TC message to broadcast topology information | |
Reduces control overhead by limiting MPR set | |
In the graph, B selects C as MPR |
Same topology as before, numbers along edges indicate available bandwidth | |
OLSR: node B will select C as its MPR | |
So all the other nodes know that they can reach B via C | |
When D is building its routing table, for destination B, it will select the route D-C-B, whose bottleneck bandwidth is 3, the worst among all the possible routes | |
Optimal route (i.e., path with maximum bottleneck bandwidth: D-F-B (bottleneck bandwidth of 10) |
Change MPR selection | ||
Limited by neighbour knowledge: direct neighbours and two-hop neighbours | ||
Assume bandwidth to neighbours and between 1-hop and 2-hop neighbours is known | ||
Change route table calculation, based on TC messages (which contain information about MPR selection) |
OLSR_R1: similar to OLSR (i.e., choose 1-hop neighbours that cover maximum number of 2-hop neighbours), tie-breaker now maximum bandwidth | |
OLSR_R2: select the best bandwidth neighbors as MPRs until all the 2-hop neighbors are covered. | |
OLSR_R3: selects the MPRs in a way such that all the 2-hop neighbors have the maximum bottleneck bandwidth path through the MPRs to the current node |
Take bandwidth into account: | ||
Maximum spanning tree (in paper) | ||
Extended Bellman-Ford (BF) shortest path | ||
Same complexity, second algorithm guarantees that we find shortest route as well | ||
Proof that algorithms indeed correctly find maximum bandwidth path | ||
If complete knowledge, proof by contradiction | ||
Can show that TC messages have enough (i.e., the important) information |
Simulation: generate networks, run OLSR algorithms, compare results against paths calculated by Link-State algorithm (i.e., complete knowledge, all-pair shortest path) | |
Network area: 1000 M ´ 1000 M | |
Number of nodes: 100 | |
Transmission range: 100 M, 200 M, 300 M | |
Bandwidth: assigned randomly | |
Results are averaged over 100 randomly generated networks |
Error rate: percentage of routes with non-optimal bandwidth | |
Average difference: for routes with non-optimal bandwidth, how far off the optimal bandwidth are we | |
Overhead: the average number of control messages transmitted per node | |
MPR count: average number of MPRs in the network |
OLSR_R1: simple change, with little additional overhead, but already significant improvement (mostly due to change to routing table calculation) | |
OLSR_R2 and OLSR_R3 seem to be perfect, paper contains proof that both MPR selection criteria allow to reliably detect optimal path | |
OLSR_R2 selects fewer MPRs, less overhead, therefore preferred | |
Transmission range has important impact: higher range means denser network but not necessarily higher overhead or error rate |
Implement OLSR_R2 in OPNET and study performance in dynamic environment (currently under way) | ||
Trigger TC messages not only when topology changes, but also when significant bandwidth changes | ||
QoS OLSR overhead will compete with data traffic for fixed bandwidth, if overhead too high, best bandwidth route may well have less available bandwidth than non-optimal best-effort OLSR route | ||
Compare QoS routing to QoS signaling in MANET such as dRSVP, INSIGNIA, SWAN | ||
Combine QoS OLSR with SWAN |